DATE_FORECAST | COUNTRY | ZIP_CODE | DATE | MODEL | VALUE | CLASS |
---|---|---|---|---|---|---|
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxxx | xxxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxxxxxxxx | xxxxxx | x |
xxxxxxxxxx | xx | xxxxx | xxxxxxxxxx | xxxxxxxxxxxx | xxxxxxx | x |
Description
Weather significantly impacts sales and eCommerce, influencing consumer behavior and purchasing patterns. By analyzing weather forecast data alongside sales data, we have identified trends so that businesses can make strategic decisions to optimize their operations. This data includes the forecast of weather-based demand for up to 10 days on daily level for a given ZIP code. In comparison to the full data set, this data sample provides information for one ZIP code. The data can be found here: "PUBLIC"."FORECAST_GFK_VIEW_EXAMPLEā€¯ The dataset has the following fields: - date_forecast: Date on which the forecast was created - country: country of the zip_code. - zip_code: Zip code for which the index was calculated for a pollen type - date: date for which the index was calculated - model: industry / product type for which the indices were calculated - value: actual impact of the forecasted weather on the given date and zip code. value of 1.15 means the demand is 15 % higher due to the weather than normally. - class: the class to which level the weather actually influences the demand more generally The definition of the class is: 1: weather reduces the demand on 10 % of the days 2: weather reduces the demand on 20 % of the days 3: weather has no influence on the demand on 40 % of the days 4: weather increased the demand on 30 % of the days We offer the following models in this dataset: - Car-Tyres - DIY-Activity - Fashion-Stationary - Fashion-Ecommerce - Fashion-Swimwear - Fashion-Athletic Apparel - Fashion-Sneakers - Fashion-Outerwear - Fashion-Umbrella - FMCG-Beverages: Beer - FMCG-Beverages: Coke - FMCG-Beverages: Coffee - FMCG-Beverages: Juice - FMCG-Beverages: Tea - FMCG-Beverages: Water - FMCG-Beverages: Wine - FMCG-Food: Bakery Goods - FMCG-Food: BBQ - FMCG-Food: Ice Cream - FMCG-Food: Snacks - FMCG-Food: Chocolate Goods - FMCG-Food: Frozen Goods - FMCG-Personal Care: Deodorant - FMCG-Personal Care: Dry Skin - FMCG-Personal Care: Insect Skin Protection - FMCG-Personal Care: Body Care - FMCG-Personal Care: Sun Protection - FMCG-Pharma: Cold Medicines - Garden Outdoor: Garden Furniture - Garden Outdoor: Garden Tools - Garden Outdoor: Grills Accessories - Garden Outdoor: Outdoor Plants
Country Coverage
(5 countries)Data Categories
- Ecommerce Data
- Ecommerce Sales Data
- Advertising Data
- Weather Forecast Data
- Sales Data
Pricing
One-off purchase |
Available |
Monthly License |
Available |
Yearly License |
Available |
Usage-based |
Not available |
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